Potential quorum-sensing inhibitor of Hafnia alvei H4—theaflavin-3,3´-digallate analyzed by virtual screening and molecular simulation

ABSTRACT Hafnia species can cause food spoilage via the quorum-sensing (QS) system. Thus, strategies that target QS in these bacteria might be a good approach to safeguard the quality of processed food. In this study, the amino acid sequence of the LasI Ha protein, a key QS regulator from Hafnia alvei H4, was used to construct its 3D structure for the virtual screening of potential QS inhibitors (QSIs) from the Bioactive Compound database. Four potential QSIs were obtained, and these were all theaflavins (TFs). Among them, theaflavin-3,3´-digallate (TF3) was found to outperform the others, displaying a higher docking score according to molecular docking analysis, and required only a sub-minimal inhibitory concentration (31.25 mM) to cause a significant decrease in the production of the autoinducer N-acyl homoserine lactone in H. alvei H4 and up to 60.5% inhibition of its motility. Furthermore, molecular simulation results indicated that TF3 could stably bind to a cavity within LasI Ha to form stable hydrogen bonds and hydrophobic interactions with various key residues of the protein to exert the inhibitory effect. Thus, TF3 may be considered a potential compound to protect against food spoilage caused by H. alvei H4 via the quorum quenching. IMPORTANCE Hafnia alvei, the main strain studied in this paper, is often isolated from spoiled foods, especially refrigerated protein-based raw foods, and is generally considered to be a spoilage bacterium whose spoilage-causing properties may be closely related to its own very strong population-sensing activity, so the strategy of quorum quenching against H. alvei H4 may be a good way to guarantee the quality of processed foods. Given the current global requirements for food safety and quality, coupled with negative consumer perceptions of the excessive inclusion of synthetic chemicals in food products, the use of natural compounds as QSIs in the storage of aquatic food products would seem more attractive.

Q uorum sensing (QS) is the "bacterial language" using chemical molecules known as autoinducers (AIs) generated during cell proliferation to facilitate cell-cell communication (1).The most prevalent family of AIs employed by Gram-negative proteobacteria is N-acyl homoserine lactone (AHL), which is synthesized by AHL-syn thases (a LuxI-type family protein) using S-adenosyl-L-methionine and acyl-acyl-carrier protein (acyl-ACP) as substrates.When the concentration of AHL reaches a threshold as a result of an increase in bacterial cell density, members of the LuxR protein family would bind to the AHL to activate the expression of downstream genes to control a vast set of relevant phenotypes, including cell motility, biofilm formation, and virulence factors (2,3).Previous research has shown that QS plays an important role in the bacteria-mediated deterioration of seafood such as giant yellow croakers (4).In addition, the QS system may also work in conjunction with other bacterial products to bring about food spoilage, and these products include bacterial proteases, lipases, and biofilms.One example of a food spoilage bacterial species is Hafnia alvei, which is an important chilling spoilage bacterium often found in decaying food.Many studies have highlighted the crucial role of the QS system-controlled virulence factors and biofilm production of H. alvei in food spoilage (5,6).Tan et al. (7) isolated H. alvei from spherical fish and detected two short-chain AHLs by lLiquid chromatograph-mass spectrometer (LC-MS): 3-oxo-C6-HSL and 3-oxo-C8-HSL.Christensen et al. (8) showed that the specific spoilage bacteria of rainbow trout were H. alvei, Serratia liquefaciens, and Pseudomonas fluorescens, which produced AHLs such as 3-oxo-C6-HSL and C6-HSL that were able to regulate protein hydrolase activity and spoilage of rainbow trout fillets.In addition, some studies found that the specific spoilage organisms (SSOs) in turbot under 4℃ refrigerated condition were P. fluorescens and H. alvei (9).In addition, although the number of H. alvei was low in aquatic products, the growth of other non-cold-tolerant bacteria was inhibited when low temperature conditions were reached, while H. alvei could become the SSO under low temperature conditions and its putrefactive ability is regulated by the QS system (9).Thus, focusing on the bacterial QS system might be a potential tactic to delay the onset of bacterial-mediated food spoilage.
Bacterial putrefaction can be avoided by natural or deliberate interference that inhibits or disrupts QS.QS inhibitors (QSIs) are chemicals that do not affect the growth of the bacteria but only block, interfere with, or inhibit the exchange of information among the bacterial cells by affecting the QS system.The best QSI is one that can be used at a concentration that is non-toxic to humans and has little impact on the growth of the bacterial cells in terms of direct killing while displaying maximum inhibition of QS activity.QSIs such as quercetin and L-carvone (10) have been found to interfere with QS primarily by competing with AHL for binding with the receptor to inhibit the expres sion of downstream genes involved in various processes, such as bioluminescence and pigment or antibiotic production (11).Another approach to interfere with the QS system is to directly inhibit the synthesis of AHLs by targeting specific AHL synthase.Such an approach would require a better understanding of the AHL synthesis process and the mechanisms of these molecules from the structural perspective.So far, the 3D structure of LasI except for the LasI Ps from Pseudomonas aeruginosa has not been determined (12).LasI Ps has less restriction on the length of the acyl chain in AHLs, as it can accommodate AHLs with an acyl chain ranging from C4 to C8.In addition, a structural study of EsaI from Pantoea stewartii has identified a hydrophobic pocket within the protein for binding the acyl chain (13,14).Since traditional methods of screening QSIs are time consuming and inefficient, a virtual screening technique has been introduced to reduce screening time.This process is based on molecular docking that involves performing a large number of rapid screenings of compounds in a small molecule library to achieve shape and energy matches between ligands and receptors (15).Although the 3D structure of H. alvei H4 LasI Ha has not been reported, the availability of its protein sequence and the 3D structures of P. aeruginosa LasI Ps and P. stewartii EsaI could provide sufficient information for constructing the 3D structure of H. alvei H4 LasI Ha via homology modeling.
In this study, we screened approximately 50,000 small molecule compounds from the MCE Bioactive Compound Library (https://www.medchemexpress.com/screening/BioactiveCompoundLibrary.html) for potential QSIs against the LasI Ha protein of H. alvei H4.One theaflavin-type (TF) compound, theaflavin-3,3´-digallate (TF3), was found to exhibit the strongest QS inhibitory activity and the lowest minimum inhibitory concentration (MIC) value.Phenotypic studies showed that TF3 could remarkably reduce the produc tion of AHLs and significantly inhibit the motility, biofilm formation, and expression of QS-related genes in these strains.In addition, the result of molecular simulation further validated the QS inhibitory effect of TF3.Given the current global requirements for food safety and quality, coupled with negative consumer perceptions of the excessive inclusion of synthetic chemicals in food products, the use of natural compounds as QSIs in the storage of aquatic food products would seem more attractive.

Bacterial strains, chemicals, and culture conditions
The bacterial strains in this study were wild-type (WT) H. alvei H4 and two mutants previously constructed in our laboratory (16), one of which lacks the lasI gene (ΔlasI), whereas the other lacks the expR gene (ΔexpR).Both wild-type and mutants were cultured in Luria-Bertani (LB) broth medium at 30°C with shaking at 150 rpm.In addition, the mini-Tn5 mutant of Chromobacterium violaceum (CV026) provided by the Chinese Academy of Agricultural Sciences (17) was also used in this study.The QS signaling molecule C6-HSL was purchased from Sigma Aldrich (St. Louis, MO, USA).All small molecule compounds for potential QSIs validation assay were purchased from MedChe mExpress (MCE, Shanghai, China) with a purity of 98%.Each compound was dissolved in sterile water or dimethyl sulfoxide (DMSO).Thirty percent DMSO and sterile water were used as controls.

Protein and ligand preparation
The amino acid sequence of H. alvei H4 LasI Ha was taken from our previous publication (18).The sequence was then submitted to SWISS-MODEL (https://swissmodel.expasy.org)(19) to construct a 3D structure.The best model with the highest similarity and GMQE (Global Model Quality Evaluation) scores was selected from the 50 models built by SWISS-MODEL.The chosen 3D structure was evaluated by using the SWISS-MODEL and Ramachandran Plot.To determine the extent of protein sequence similarity between H. alvei H4 LasI Ha and related proteins from other species, its amino acid sequence was aligned with the sequences of the most frequently studied AHL synthases, LuxI, RhlI, and EsaI as well as the P. aeruginosa LasI Ps using CLUSTALW (https://www.genome.jp/tools-bin/clustalw), and the final alignment result was displayed by ESPript 3 (https:// espript.ibcp.fr/ESPript/ESPript/index.php).
The LasI Ha protein was then subjected to further fine-tuning, such as hydrogenation, removal of water molecules, and repair of missing residues and side chains due to missing residues in the template side chain using the Protein Preparation Wizard module.Subsequently, energy optimization (OPLS2005 force field, root mean square deviation [RMSD] = 0.30 Å) was performed.The final version of the protein was used to make lattice files with the Receptor Grid Generation module for subsequent docking.All other parameters were set to default.
The 2D format of MCE Bioactive Compound Library was processed by hydrogenation and energy optimization through the LigPrep Module of Schrödinger software 11.4 (LLC, NY, USA, 2018-4), and the 3D structure was output for virtual screening.MCE Bioactive Compound Library contains ~50 k compounds distributed in the natural product library, drug and food homology library, food-derived component library, and food additive library.

Virtual screening
Structure-based virtual screening against approximately 50,000 compounds was performed by Schrödinger Maestro 11.4 software to identify potent molecules that could interact with the active pocket of LasI Ha .The Virtual Screening Workflow module was used for the screening process.After the import of the 50,000 compounds, molecu lar docking was performed with the Glide module to examine the docking between receptor and ligand molecules based on geometric and energy matching.First, the compounds in MCE Bioactive Compound Library were screened by the high-throughput screening mode of the Glide module, and then the top 30% of the small molecule compounds were selected by the standard (SP) mode for the second round of screening.
Next, the compounds from the top 30% score were selected for the third round of screening with high precision (XP) mode to obtain a ranking for the compounds.The binding ability of each compound and the structures of the protein and compounds were manually reviewed to select the top 200 output compounds from MCE Bioactive Compound Library.After completing the virtual screen, a total of 11 compounds that yielded high docking scores ( >8) or normally found as food components were selected from the top 200 for further experimental validation.

Identification of anti-QS activity
Anti-QS activity assay was conducted according to a previously described method (20).A sample of CV026 suspension was mixed with nutrient agar medium at a cell-to-medium ratio of 1:50.This was followed by the addition of 20 mg/mL C6-HSL, and the mixture was then poured onto a plate.After solidification, wells were punched in the agar with Oxford cups.Subsequently, 60 µL of a test compound was added per well.Anti-QS activity was assessed by the presence of a colorless, opaque but turbid halo around the well.

MIC assay and growth curves
A series of twofold dilutions was prepared for each compound in a 96-well plate to give a final concentration range from 0.03 to 2.0 mM for CV026 and 3-600 µM for three H. alvei H4 strains.A suspension of an overnight bacterial culture was added to each well containing a test compound and then incubated for 24 h.The MIC value was defined as the lowest concentration of the compound that totally inhibited microbial growth (21).
To determine the effect of TF3 on test strains, a culture of each strain was treated with or without TF3 at sub-MICs and incubated for 36 h (22).The OD 600 of the culture was monitored every 6 h by SpectraMax M2 Multifunction microplate reader (Meigu Molecular Instrument Co., Ltd., Shanghai, China).

Extraction and detection of AHLs in WT and ΔexpR
WT and ∆expR cultures treated with different concentrations of TF3 (7.8 mM, 15.6 mM, and 31.25 mM) were incubated for 24 h, and AHLs were then extracted according to Li et al. (18).After that, the extracted AHLs were added to each well (20 µL), which were punched in fresh agar plates containing CV026, and the plates were incubated at 30℃ for 24 h.The presence of AHL was detected by the appearance of the purple zone, and the level of AHL was quantified in terms of the size of the purple zone (23).

The determination of motility assay of WT, ΔlasI, and ΔexpR
The assay for H. alvei H4 motility (swimming and swarming) was performed as previ ously described (24) with slight modifications.In brief, WT, ΔlasI, and ΔexpR were each incubated with 7.8 mM, 15.6 mM, and 31.25 mM TF3 overnight, and 3 mL of the culture was placed at the center of a swimming agar plate (agar 0.3%, tryptone 1%, and NaCl 0.5%) or swarming agar plate (Trypticase soy broth [TSB] medium, 0.5% agar).The plate was incubated for 48 h, and flagellar motility was determined by measuring the diameter (centimeter) of free cells (25).The twitching motility assay was performed exactly as described by Déziel et al. (26).

Biofilm biomass
Three strains were first tested for their ability to form biofilms within 72 h (27).Briefly, bacterial cultures and LB medium were transferred into 96-well polystyrene microplates at a ratio of 1:100, and biofilms were formed over 12,24,36,48,60, and 72 h.The formed mature biofilms were washed three times with sterile 1× phosphate-buffered saline (PBS), then fixed with 200 µL of methanol for 15 min, dried at 60℃, and stained with 200 µL of 0.1% crystal violet (CV) for 15 min.The plates were again washed three times with deionized water to remove excess dye and then dried at 60℃.The biofilm formed on the plates was dissolved by adding 200 µL of 33% acetic acid (per well) and then incubated for 20 min at room temperature.The absorbance of the plates was then measured at 590 nm using a spectrophotometer (Molecular Devices, San Francisco, CA, USA).

The viability of sessile cells in biofilms
The viability of the biofilm cells was determined using 3-(4,5-dimethylthiazol-2-yl) −2,5-diphenyl tetrazolium bromide (MTT) assay developed by Krom et al. (28).One milliliter of culture medium and 0.1 mL of 5 mg/mL MTT solution were added to each well and incubated at 30℃ for 4 h.After discarding the culture supernatant, 1 mL of DMSO was added to each well to dissolve MTT for 30 min.The optical density of each well was measured at 570 nm.

Enumeration of planktonic and sessile cells
CFU counts were used to determine the number of cultivable cells in planktonic culture and disrupted biofilms.Briefly, a total of 2 mL of three target bacterial culture bio films with or without TF3 was prepared.After 24 h, the supernatant of each well was transferred to a centrifuge tube and serially diluted to obtain a planktonic cell suspen sion.The dilution ratios of 10 −5 , 10 −6 , and 10 −7 of target strains were measured using LB agar plates.Furthermore, the number of sessile bacteria in biofilms was quantified using "bead vortexing method" (29).Adherent biofilms on glass tubes were washed with saline before adding 2 mL 0.9% saline solution and glass beads and vortexed vigorously to detach the biofilm cells.After serial dilutions of the isolated cells on LB agar plates, colonies of the target strains 10 −3 , 10 −5 , and 10 −7 were counted in three dilutions.

Real-time quantitative polymerase chain reaction assay
Real-time quantitative polymerase chain reaction (RT-qPCR) was carried out to determine the effect of TF3 on the expression of some QS-mediated genes (cheA, fliC, and motA).WT, ΔlasI, and ΔexpR were each incubated with 7.8 mM, 15.6 mM, and 31.25 mM TF3, respectively, overnight until mid-log phase; then, the cells were harvested at the same OD 600 nm (around 0.7), and total RNA was extracted using a Tiangen RNA extraction kit (Tiangen Biotech, Beijing, China).The extracted RNA was converted to cDNA using a PrimeScriptIM RT Reagent Kit with gDNA Eraser (TaKaRa, Dalian, China).RT-qPCR was performed with a BIO-RAD MyiQTM2 Real-Time Detection System and SYBR Green PCR Master Mix (TaKaRa, Dalian, China) using the primers listed in Table 1.The 16S rRNA was used as an internal control, and the fold change of each target gene was determined using the 2 −∆∆Ct method as previously described (30).

Molecular docking
The mechanism by which the test compound inhibited LasI Ha /ExpR was analyzed by Schrödinger Maestro 11.4.Two-dimensional maps were used to obtain the details of the interaction between the protein active site and the test compound.The molecular docking result was then visualized and analyzed by PyMoL software (1.3r1, DeLano Scientific LLC, South San Francisco, CA, USA).

Molecular dynamics simulation
Molecular dynamics (MD) studies of protein-ligand complexes were performed following the method of Musyoka et al. (31).MD simulations were performed using the Gromacs 2018.4 program at a constant temperature and pressure.For the proteins, the mber14SB all-atom force field was applied, whereas, for all test compounds, the AMBER-based GAFF force field with the TIP3P water model was used.During the MD simulation, all bonds involving hydrogen atoms were constrained using the LINCS algorithm with an integration step size of 2 fs.Electrostatic interactions were calculated by the (Particle-Mesh Ewald) PME method.The truncation value for non-bonded interactions was set at 10 Å and updated every 10 steps.The V-rescale temperature coupling method was used to control the simulated temperature at 298.15 K, and the pressure was controlled at 1 bar by the Parrinella-Rahman method (32).First, the energy of the two systems was minimized by the steepest descent method to eliminate the contact between atoms that were too close.Then, the canonical ensemble (NVT) and constant-pressure, constant-temperature (NPT) equilibrium simulations were carried out at 298.15 K for 1 ns, respectively.Finally, the system was simulated by MD for 50 ns, and the conformation was saved every 10 ps.The visualization of the simulation results was done through the Gromacs inline program and Visual Molecular Dynamics program.

Statistical analysis
Analysis of variance (ANOVA) was performed using SPSS Statistics 20.0, and differences between variables were tested by one-way ANOVA and Tukey's test.All graphical data were drawn with Origin Pro 9.0.All experiments were replicated in triplicate, and all analyses were performed with three replicates.

Construction of the 3D structure of LasI Ha
The 3D structure of LasI Ha was eventually obtained by homology modeling.First, known proteins with structures homologous to H. alvei H4 LasI Ha sequence were found by Blast, and those with highest homology were selected for full sequence comparison.Finally, proteins with more than 30% similarity to LasI Ha were used as templates to build the model, since a model built from such a criterion would well represent the actual structure of the target protein.  4, Arg 68 , and Phe 82 are highly conserved residues (Fig. 1B).According to the 3D structure of P. aeruginosa LasI Ps (12), Arg 23 , Phe 27 , and Trp 33 form a closed S-adenosine-L-methionine substrate binding pocket.Compared with P. aerugi nosa LasI Ps , the N-terminal region visible in the crystal structure of P. stewartii EsaI (13) has a more open conformation that makes up a highly mobile active site groove and is formed by the same three conserved residues, Arg 24 , Phe 28 , and Trp 34 in this case.The remaining conserved residues, Asp 45 and Arg 68 , aggregate to form an ion-pair network to stabilize the interactions within the N-terminal structural domain.
The predicted structure of H. alvei H4 LasI Ha also consisted of nine β chains surround ing eight α sheets in a highly distorted open conformation (Fig. 1C), and the function dictated by this structure could be predicted from the reported crystal structure of P. aeruginosa LasI Ps , which consists of three α helixes supporting a six-chain β sheet platform to form a V-shaped substrate-binding site.This results in a tunnel that can accommodate the acyl-ACP chain without any apparent conformational change, which  is in contrast to the restricted hydrophobic pocket in EsaI.In short, the hydropho bic channel of LasI Ps might also provide valuable information for the design and/or detection of specific LasI-type inhibitors.
The Ramachandran plot analysis of LasI Ha showed a total of 98.4% of residues in the favorable and additional allowed regions, and 1.6% were in disallowed regions (Fig. 1D), indicating a good quality model for the predicted LasI Ha structure.

Virtual screening results and compound selection for further experimental validation
The structure and scoring values of some of the top 200 compounds identified by virtual screening are shown in Table 3.The higher the absolute value of the molecular docking score, the stronger the binding force between the compound and protein.Among the top 200 compounds were two theaflavins, theaflavin-3,3´-digallate (TF3, ranked second) and theaflavin-3´-gallate (TF2b, ranked 53 rd ), which exhibited high docking scores as determined from the binding with LasI Ha .Notably, theaflavin has four variants (theaflavin [TF1], theaflavin-3-gallate [TF2a], TF2b, and TF3), but only TF3 and TF2b were listed in the top 200, and this may be due to incomplete coverage of the small molecule database.Thus, all four theaflavins were examined for their antibacterial and anti-QS activities, since we wanted to determine whether the conserved structures or some specific groups of these theaflavins might be important for their activities.The respective structural formula and 2D and 3D structures of four TFs are shown in Fig. 2. In addition, another 10 small molecules with high docking scores or safe for food application were also selected from the top 200 for further experimental verification (Table 3).

MIC and anti-QS activity of four potential QSIs
The MIC of each test compound was measured to ensure the final concentrations of these compounds used in the experiments did not affect bacterial growth.All four TFs were found to display good antibacterial activity, with TF3 yielding the strongest activity and naturally, the lowest MIC value (125 µM) compared with TF1 (300 µM), TF2a (150 µM), and TF2b (600 µM).The other 10 compounds did not show any obvious antibacterial activity at the highest concentrations tested, 600 mM for the three H. alvei H4 strains and 2 mM for CV026 (Table 4).
The above experiments showed promising MIC values for the four TFs, while MIC values did not correlate with whether the small molecule compounds themselves had anti-QS activity, so further evaluation of the anti-QS activity of all tested compounds is needed.CV026 is unable to produce AHLs but can bind to exogenous short-chain AHLs, resulting in the secretion of violacein.From the plate assay, a relatively obvious distinct cloudy halo was observed around the wells containing poliumoside or TF3 (Fig. 3A), whereas only faint halos appeared around the wells containing stevioside, rubusoside, or NAD + .Since the size of the halo is a direct indication of the level of AHL in the sample, the degree of translucence and the size of the halo would reflect the anti-QS activity of the test compound (33).The results suggested that TF3 exerted excellent anti-QS activity, and since the other compounds did not affect the level of violacein in the plate because they lacked the anti-CV026 activity, these compounds were excluded from the subsequent QS phenotype experiments.TF3, a type of theaflavins derived from black tea (34), is widely considered to be the most effective bioactive component of TFs, as it is capable of acting as an antioxidant, antibacterial, and antitumor agent (35).Again, TF3 was found to display a stronger inhibitory effect on violacein produc tion by CV026 than the other three TFs (Fig. 4), indicating its stronger anti-QS effect.Interestingly, the structures of the four TFs differ only in the position and number of gallic acid at the C-3-and C-3´ positions, but the overall 3D structures of these molecules are very different (Fig. 2).Since the structure of a molecule also determines its function, the different antibacterial and anti-QS activities observed for these four TFs could be attributed to the differences in their 3D structures.Both TFs and polyphenols (TP) are polyphenols that are found in tea, especially black tea, and TPs have been considered a novel class of non-antibiotic QSIs (36).However, the exact structure-function relationship of TFs responsible for their anti-QS active effects had not been previously determined, so it was important to further analyze whether the functional differences of the four TFs were due to the polyhydroxyl group of the theaflavin or gallic acid component.

Effect of TF3 on AHL synthesis
To verify the inhibitory effect of TF3 against H. alvei H4 was primarily a result of it targeting LasI Ha , the effect of TF3 on AHL production in ∆expR was compared with its effect on the WT.Analysis of the growth of H. alvei H4 in the presence of TF3 revealed that inhibition of growth of either WT or the two mutants did not occur except at 62.5 µM concentration of TF3. (Fig. 3B).Since ∆lasI could not produce AHL (data not shown), it was not subjected to an AHL assay analysis.In the AHL assay, CV026 could respond only in the presence of exogenous AHLs and, after which, produced the characteristic violet pigment, violacein.Thus, its absence is a direct indication of the absence of AHL.In WT, the production of violacein was reduced, and the higher the TF3 concentration, the stronger the reduction, as revealed by a weaker purple zone in the plate (Fig. 5A).As for ∆expR, the reduction of violacein production was most obvious at 15.6 mM TF3, since more violacein was produced when the concentration of TF3 was increased to 32.25 mM, and this was rather unexpected.Geng et al. (37) found that treatment of P. aeruginosa with different sub-MIC concentrations of luteolin reduced the production of OdDHL, but surprisingly, the effect of 100 µM luteolin was much greater than the effect of 200 µM.The authors considered the higher concentration of luteolin may affect the activity of RsaL (a transcriptional repressor of the lasI gene), which can then further affect the level of OdDHL, since RsaL can also regulate the production of OdDHL (38).We have previously identified several DNA-binding transcrip tional repressors (AcrR, fur, DeoR, and PurR) in H. alvei H4 (18).We speculated that knockout of the expR gene allowed the TF3-LasI Ha complex to exert an inhibitory effect on AHL production at 7.8 mM and 15.6 mM, but when the TF concentration increased to 31.25 mM, it may have reached the threshold concentration that led to the relief of the repressive effects of those transcriptional repressors on the lasI gene, thereby prompting an increase in the expression of LasI Ha and, hence, more production of AHL.The verification of this speculation requires further study.

Effect of TF3 on the motility of H. alvei H4
Flagellar motility such as swimming and swarming is the main form of locomotion for most mobile bacteria, with the benefit of helping bacterial cells obtain nutrients, form biofilms, and colonize a surface (39).Similarly, twitching motility based on type IV pilus has also been shown to be required for the initial attachment of bacterial cells during biofilm formation (40).As for H. alvei H4, its motility was clearly inhibited by TF3, but the effect of TF3 appeared to vary for the different types of motilities and for the different bacterial strains.All three strains exhibited progressively reduced swimming motility with increasing TF3 concentrations, but the wild type was most affected (Fig. 5B).Only the WT and ∆expR showed severe loss of swarming motility with increasing TF3 concentrations (Fig. 5C).Furthermore, TF3 exerted a much stronger inhibition on the swarming motility of ∆expR than on the swarming motility of WT at 15.6 mM and 31.25 mM compared with 7.8 mM (Fig. 5C).The swarming motility of ∆lasI appeared to be unaffected by TF3 because it did not change significantly upon the addition of TF3.The reduction in swimming and swarming motility was reflected by a reduction of the migrated distance around the point of bacterial inoculation, and in this study, a concentration-dependent decrease in migration distance was observed for both swarming and swimming in WT and ∆expR.Since swarming is driven by multiple flagellar while swimming requires only one flagellum (41), we speculated that TF3 might not only affect the number or activity of the flagella of individual cells but could also affect the collective movement of a large number of cells.In addition, the inhibitory effect of TF3 on the motility of ∆lasI was relatively small, and there were no significant differences among the different concentrations of TF3.This may also indirectly verify that TF3 could exert a better inhibitory effect by binding to LasI Ha .Different motility phenotypes correspond to different activation mechanisms.Swimming motility is a mode of bacterial motility driven by rotating flagella, and it occurs when individual cells move in a liquid environment, whereas swarming motility is operationally defined as rapid multicellular bacterial surface motion driven by rotating flagella (41).Different from the inhibition of motility by TF3, some studies also observed motility inhibition by other phenolic compounds, like proanthocyanidins and tannins that completely inhibited swarming, but did not prevent swimming, and trans-resveratrol that was more active against swarming than against swimming motility (42), further suggesting that these movement phenotypes have different activation mechanisms and the inhibitory effects of different phenolics on motility were also not completely similar.Furthermore, ∆lasI formed a smaller and denser zone at the plate-agar interface than the WT and ∆expR when the cells punctured the agar layer at the sample spots (Fig. 5D).In general, none of the three strains had strong twitching motility.Crystalline violet staining performed on the cells that remained on the plate after the agar had been scraped off indicated that the attached cells were closely associated with the twitchy region (Fig. 5E).Notably, the ability of each strain to attach to the plate surface was diminished after TF3 treatment, as shown by the smaller twitch zones in the plate before the agar was removed.The change in twitching motility exhibited by the bacterial cells was more clearly visualized after staining with crystal violet.TF3 could, therefore, affect type IV pilus or the related type II secretion system.

Effect of TF3 on the biofilm formation of H. alvei H4
Bacterial biofilm formation is a dynamic process with distinct phases of development (43).In this study, the CV assay was used to examine the dynamics of biofilm , rubusoside (7), NAD+ (8), eriocitrin (9), disodium 5'-inosinate (10), folic acid (11), Sunset yellow (12), 30% DMSO (13), and sterile water.(B) Effects of TF-3 on the growth of H. alvei H4 WT, ∆lasI, and ∆expR at different concentrations.All data were expressed as means ± SD (n = 3).The red X means that when the concentration of TF3 was 62.5 µM, it has started to affect the growth of three strains.development of H. alvei H4 WT, ΔexpR, and ΔlasI during 72-h incubation (Fig. 6A-C).The results showed that WT and ΔlasI continued to grow up to 72 h, whereas ΔexpR flatten after 48 h (Fig. 6A).Both ΔexpR and ΔlasI showed lower biofilm formation than WT, with a more pronounced decrease in ΔlasI in particular (Fig. 6B), consistent with previous laboratory result (6).Furthermore, previous studies have shown that cell viability was closely related to biofilm formation (44), and the differences in these strains in reaching the maturation stage were highly attributed to strain heterogeneity (45).The results showed that biofilms of WT and ΔexpR grew rapidly with increasing incubation time and reached a maximum at 48 h (Fig. 6B) when the viability of their corresponding biofilm cells also reached a maximum (Fig. 6C), and both biofilm formation and biofilm activity of ΔlasI reached a maximum at 60 h.Our experimental results also confirmed the above view (Fig. 6B and C).We also observed that the biofilm of all three strains decreased after 60 h of incubation, indicating that the biofilm started to disintegrate.Throughout the biofilm formation process, we chose the biofilm incubated for 24 h to study the inhibitory effect of exogenous addition of TF3 on WT, ΔexpR, and ΔlasI.
Independently of the fact that ΔexpR and ΔlasI formed less biofilm than WT, it showed that addition of different concentrations of TF3 respectively inhibited biofilm formation in WT, ΔexpR, and ΔlasI, and the reduction in biofilm amount occurred in a concentra tion-dependent manner (Fig. 6D), with the highest inhibition rate of 67.4% in WT by 31.25 µM TF3 (Fig. 6E).MTT assay was employed to explore the cell viability in WT, ΔexpR, and ΔlasI biofilms.The biofilm cell viability of ΔexpR and ΔlasI was significantly lower than that of WT, while the addition of sub-MICs of TF3 significantly reduced the OD 570 nm values of the three bacteria (Fig. 6F).The percentage of inhibition of cell viability by TF3 was high, with 31.25 µM of TF3 inhibiting WT and ∆expR by more than 60% (Fig. 6G), which was the same trend as the amount of biofilm formation (Fig. 6E).Furthermore, since biofilm is a dynamic formation process in which sessile and planktonic cells are inter-converted (43), we therefore further determined the effect of adding TF3 on sessile and planktonic cells in the solution.Analysis of this assay revealed that WT had more sessile and planktonic cells than ΔlasI and ∆expR (Fig. 6H and I).After the addition of sub-MICs of TF3, WT and ∆expR inhibited sessile and planktonic cells in the biofilm in a concentration-dependent manner, and ΔlasI appeared to be unaffected by TF3 because it did not change significantly upon the addition of TF3, which corresponds to the results in Fig. 6D and F and is also consistent with the trend in aforementioned motility experiments.

Effect of TF3 on the expression of QS-regulated genes
RT-qPCR was used to determine the expression of QS-related genes in H. alvei H4 as a means to examine the effect of TF3 on QS at the transcriptional level.Three QS-related motility factors, fliC, cheA, and motA, were chosen for this analysis, and their primers were all optimized to 90-110% efficiency using the standard curve of DNA.Overall, the results revealed a significant reduction in fliC, cheA, and motA mRNA levels in both WT and the two mutants after exposure to increasing concentrations of TF3 (Fig. 7), indicating the strong inhibitory effect of TF3 on the expression of these genes, and this inhibitory effect was also reflected in the weakened motility of the bacterial cells (Fig. 5).Surprisingly, the inhibitory effect of TF3 on these genes was not entirely concentration dependent, at least in the case of the WT, since the reduction in fliC and cheA mRNAs was much less severe at the highest concentration (31.25 mM) of TF3.As for ∆expR, the effect of TF3 on the mRNA levels of fliC, cheA, and motA was quite similar between 15.6 mM and 31.25 mM, as both concentrations reduced the mRNA levels to a similar degree.The RT-qPCR data clearly showed the inhibitory effect of TF3 on the expression of the QS-mediated genes.Studies have shown that, in Escherichia coli, the fliC gene codes for a flagellar protein (FliC) required for the assembly of flagellar composition, whereas MotA, the product of the motA gene, in a component of the flagellar motor (41,46), while CheA, the product of the cheA gene, is a histidine kinase involved in most of the signal transduction in bacteria.When a chemotactic receptor senses an environmental signal and triggers a stimulus in response, the stimulus signal is transmitted to the flagellar motor via CheA and CheY (47).Therefore, in a complex environment, flagellated bacteria will rotate clockwise and counterclockwise via the flagellar motor to achieve bacterial chemotaxis.We speculated that the proteins encoded by these genes in H. alvei H4 may also perform similar functions to the corresponding proteins in E. coli.The data suggested that after the inhibitory effect of the exogenous inhibitor reached a certain threshold, a higher concentration of inhibitor would have no further inhibitory effect on the expression of these genes.It also suggested that other factors may be at play, giving rise to a more complex regulatory network of motility, which requires further study to gain more insight.

QS inhibitory mechanism analysis of TF3 binding LasI Ha by molecular docking
To determine the molecular basis of the interaction between TF3 and LasI Ha , molecu lar docking was used to probe the binding between the two molecules.Molecular docking techniques are widely used in the field of structural molecular biology and in the screening and development of new drugs (48) to identify the binding modes or static interaction forces between ligands and proteins.The result obtained from molecular docking revealed clear ligand-protein interaction for the TF3-LasI Ha complex.Fig. 8A shows the best model for the TF3-LasI Ha complex, with specific hydrogen bonds formed between TF3 and Glu 114 , Ile 149 , Ser 119 , Lys 105 , and Ser 193 , and that these bonds were contributed by the hydroxyl groups of both the theaflavin and gallic acid components of the TF3 structure.The ligand-protein interaction in the TF3-LasI Ha complex depicted by the two-dimensional interaction map was clearly illustrated.The hydrogen bond lengths formed between TF3 and LasI Ha ranged from 1.9 to 2.7 Å, with the TF3-Ser 193 bond (1.9 Å) and TF3-Glu 114 bond (2.0 Å) being stronger than the other hydrogen bonds.Since the shorter the bond length, the lower the resistance and the more stable the structure, the hydrogen bonds between TF3 and Ser 193 and Glu 114 were considered the most stable, and they may play an important role in the interaction between LasI Ha and TF3.In the TF3-LasI complex, TF-3 was wrapped in the hydrophobic pocket around production of AHLs.This was consistent with the stronger inhibition of QS activity of WT and ΔexpR compared with ΔlasI.

QS inhibitory mechanism of TF3 as analyzed by molecular dynamics simula tion
Finally, the overall conformational and stability changes of TF3 upon binding to LasI Ha and the effect of solvent on the stability of this system were analyzed via MD simula tions.Specifically, a 50-ns molecular dynamics simulation of the LasI Ha -TF3 complex was performed under physiological conditions to understand the dynamic mechanism of the LasI Ha -TF3 interaction.
The RMSD value of the LasI Ha -TF3 system rose rapidly from the beginning of the simulation, and this may be the result of the protein being affected by the ligand and interacting with the water molecules in the environment.The RMSD value of the system subsequently became stable at around 30 ns, indicating that the complex formed by the protein and ligand was stable in a solvent system (Fig. 9A).
In addition, the determination of the local structural flexibility of LasI Ha in the solo state and after binding to TF3 was performed by plotting the average fluctuations of all residues as the root mean square fluctuations (RMSF).The RMSF values of the residues in the ligand-binding region (two red boxes) were significantly smaller than those of the residues in the other regions (Fig. 9B), indicating that after the binding of the ligand to the active pocket of the protein, the ligand interacted significantly with the surrounding residues of the protein, consequently reducing the flexibility of these residues and further decreasing the overall flexibility of the protein.Lower flexibility may be more conducive to more stable substrate binding.
Hydrogen bonds play an important stabilizing role in noncovalent interactions between proteins and ligands (50).The result we obtained indicated that at the beginning of the simulation, the number of hydrogen bonds between LasI Ha and TF3 fluctuated significantly until the system reached 30 ns.By that time, the number of these hydrogen bonds became stable, indicating that a stable conformation was attained for the TF3-LasI Ha complex (Fig. 9C).
Changes in the secondary structure of the whole protein during the simulation were probed with the STRIDE algorithm to obtain the secondary structure with the most significant deviation during the simulation under known external stress (51).In such analysis, as the protein is subjected to additional pressure over the simulation, different color bands can be observed, which represent changes occurring in the secondary structure of the protein.In the case of LasI Ha , following TF3 binding, the α helix and β fold in the protein structure increased (Fig. 9D), indicating that the binding of a ligand to the protein could enhance the stability of the overall structure of the protein.
In addition, the longitudinal perspective on the binding system of small molecules to proteins and the results of different indicators throughout the simulation revealed a stable dynamic simulation process and so was the binding of small molecules to proteins, and no small molecule was found to detach from the protein (Fig. 9E), which also structurally explained the effectiveness of the LasI Ha -TF3 complex in being able to exert a sustained inhibitory effect.

Conclusion
In this study, a highly active QSI against the QS system of H. alvei H4 was obtained by a virtual screening method based on the molecular docking technique.The obtained QSI, TF3, is found in black tea, and it is regarded as the most effective bioactive compound among the various TFs identified in tea.At sub-MIC concentrations, TF3 reduced the synthesis of AHLs and led to a remarkable down-regulation of QS gene expression and inhibition of bacterial phenotypes.The high anti-QS activity of TF3 stemmed from its strong interaction with LasI Ha to form a stable TF3-LasI Ha complex.Therefore, TF3 could be considered a potential QSI with excellent antimicrobial properties, and it could act as a base compound for the development of novel QSIs and their application in food preservation.

FIG 1 (
FIG 1 (A) Alignment of the template (1k4j.1.A) and target (LasI Ha ) sequences.The color from blue to red indicates the N-to C-terminal positions of residues within the sequence.(B) Sequence alignment of the AHL-synthase EsaI, LasI Ha , RhlI, LasI Ps , and LuxI (protein sequence access address: https://www.uniprot.org/).Conserved residues are shaded, while residues in boxes are conservative substitution.(C) Ribbon representation of the 3D structure of LasI Ha obtained via modeling.Each colored portion of the structure corresponds to its location within the primary structure shown in A. (D) Ramachandran plot analysis of LasI Ha protein.

FIG 5
FIG 5 Effect of different concentrations of TF3 on the anti-QS phenotype of H. alvei H4. (A) Effect of TF3 on the production of AHLs in WT and ΔexpR.The plot compares the synthesis of AHLs in terms of the zone diameter.Inhibition of the swimming motility (B) and swarming motility (C) of WT, ΔexpR, and ΔlasI.In both panels B and C, the plots compare the extent of motility in terms of the migration distance of the bacteria from the point of inoculation.(D) Inhibition of twitching motility of WT, ΔexpR and ΔlasI.(E) Plate surface map of twitch area stained with crystal violet after the removal of agar from the plate in panel D. Statistical significance (**) was considered at P < 0.01 level, while a, b, c, and d represent significant differences between groups.

FIG 6 (
FIG 6 (A, B, and C) Dynamic formation of biofilms formation of H. alvei H4 WT, ∆lasI, and ∆expR without TF3.(A) The OD 600 nm value of bacterial growth of WT, ∆lasI, and ∆expR.(B) The OD 590 nm value of biofilm formation amount of WT, ∆lasI, and ∆expR.. (C) The OD 570 nm value of biofilm cell viability of WT, ∆lasI, and ∆expR.(D) Effect of TF3 on the biofilm formation by CV assay.(E) Percentage of inhibition of TF3 on biofilm formation in WT, ΔexpR, and ΔlasI.(F) Effect of TF3 on the cell viability by MTT assay.(G) Percentage of inhibition of TF3 on cell viability in WT, ΔexpR, and ΔlasI.(H) Effect of TF3 on the biofilm cells by enumeration assay.(I) Effect of TF3 on the planktonic cells by enumeration assay.Statistical significance (**) was considered at P < 0.01 level, while a, b, c, and d represent significant differences between groups.

FIG 8
FIG 8 Molecular docking analysis of the binding of TF3 and other TFs with LasI Ha and ExpR.(A) TF3-LasI Ha complex.(B) TF3-ExpR complex.(C) TF1-LasI Ha complex.(D) TF2a-LasI Ha complex.(E) TF2b-LasI Ha complex.Each inhibitor-protein complex is shown as a surface representation mode, followed by a close-up view of the binding between the inhibitor and residues within the binding pocket of the protein in both ribbon and two-dimensional representations.The gallic acid structure is circled by the gray dashed circles in the two-dimensional diagram.All hydrogen bonds between TFs and respective residues in the protein are indicated in red dashed lines.

FIG 9
FIG 9 Molecular dynamics of LasI Ha upon TF3 binding.(A) RMSD of LasI Ha alone and in the presence of TF3 as a function of simulation time.(B) Root mean square fluctuation (RMSF) of LasI Ha upon TF3 binding.(C) Number of intermolecular hydrogen bonds between LasI Ha and TF3 as a function of simulation time.(D) Changes in LasI Ha secondary structure in the LasI Ha -TF3 complex as a function of simulation time.Time frames are shown on the X-axis, and amino acid residue numbers are shown on the Y-axis.Coil represents the loop or random coil; B-sheet and B-bridge represent the β fold; A-helix, 5-helix, and 3-helix all represent the α-helices; and turn and bend represent the β-turn.(E) Configuration of TF3 around the LasI Ha protein during 50 ns of MD simulation; the blue border is the box of the simulation system.

TABLE 1 H
. alvei H4 gene-specific primers used in RT-qPCR Table2lists the templates with high GMQE and QMEAN scores as well as sequence identity (%).The template 1k4j.1.A showed the highest GMQE (0.83) and QMEAN (−1.83) scores and shared a high sequence identity (64.6%) with LasI Ha .Therefore, chain A of 1k4j was selected as the final template for the homology modeling of LasI Ha , and the sequence alignment of LasI Ha with the 1k4j template is shown in Fig.1A.In addition, the alignment of H. alvei H4 LasI Ha with different AHL synthases revealed that Arg 23 , Phe 27 , Trp33, Asp

TABLE 2
Three-dimensional structures and descriptions of H.

TABLE 3
QSIs identified from MCE bioactive compound library by the virtual screening with LasI as the target protein

TABLE 4
Minimum inhibitory concentration of the compounds that are potential QSIs